Hacktoberfest 2026：维护者为十月标记出来的 issue，仍然开放、适合新手。 浏览 Hacktoberfest issue

[Lane 2 · kotlin-core] F1Score metric

未关闭
  • 哪里不对？
#1,229 0 条评论 0 个 reaction 已指派 0 人 在 GitHub 查看

维护者通常 1 天内回复

还没有人认领这个 Issue。

评估

难度
3/5
预计耗时
1-3 小时
新手友好度
57/100
Issue 类型
功能
描述清晰度
描述清楚
活跃度
冷清
技术栈
kotlin
领域
machine-learning

调研方向

Read Precision.kt and Recall.kt from the blocking issues, then inspect the accumulator and Metric.kt before implementing F1Score.kt at the specified core path. Add the specified tests in F1ScoreTest.kt and run the JVM metrics tests plus the API dump command. Done means the listed cases pass and the public API appears in the dump; this issue is blocked by #1227 and #1228 (or at least #1226).

由索引模型根据 Issue 内容生成。

描述

coding good first issue size:s skill:kotlin-core sub-issue training

Sub-issue of #1222 (Precision, Recall and F1Score metrics).

Lane: 2 · Kotlin core
Skill needed: Kotlin. Read Precision.kt / Recall.kt from #1227 / #1228 first — F1Score composes their formulas over one accumulator.
Size: s (~2–3 h)
Blocked by: #1227 and #1228 (or at least #1226 — F1 only needs the accumulator, but its tests cross-check against Precision/Recall).

What to do

  1. Add public class F1Score(averaging: Averaging = Averaging.MACRO, dim: Int = -1, threshold: Float? = null) : Metric
    in skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/lang/nn/metrics/F1Score.kt:
    • override val name = "f1";
    • one private ConfusionMatrixAccumulator — do not hold a Precision and a
      Recall and call update() on both; that walks every batch twice;
    • compute() = reduce(acc.counts(), averaging) { tp, fp, fn -> per-class F1 } where
      per-class F1 = 2·tp / (2·tp + fp + fn) (algebraically 2PR/(P+R), but this form has
      a single zero-division check: 0.0 when 2·tp + fp + fn == 0).
    • Note that for MACRO, F1 is the mean of per-class F1s — not the F1 of macro-P and
      macro-R. Say so in the KDoc; it is the most common F1 implementation mistake.
  2. Factories: public fun f1Score(averaging: Averaging = Averaging.MACRO, dim: Int = -1): Metric
    and public fun binaryF1Score(threshold: Float = 0.5f): Metric.
  3. Tests in skainet-backends/skainet-backend-cpu/src/commonTest/kotlin/sk/ainet/exec/metrics/F1ScoreTest.kt,
    reference fixture pred [2,0,1,2], target [2,1,1,0]:
    • macro F1 → 0.4444 (per-class [0, 0.667, 0.667]), and assert it is not
      2·0.5·0.5/(0.5+0.5) = 0.5 — the mean-of-F1s vs. F1-of-means trap;
    • micro F1 → 0.5, equal to micro precision, micro recall and accuracy;
    • binary with threshold on 2-class logits, hand-computed;
    • a class with TP+FP+FN = 0 contributes 0.0; fresh metric compute() is 0.0;
    • accumulation across two update() calls equals one concatenated call;
    • computeForBatch (from Metric.kt) round-trips through reset().
  4. ./gradlew :skainet-lang:skainet-lang-core:apiDump and
    ./gradlew :skainet-backends:skainet-backend-cpu:jvmTest --tests "sk.ainet.exec.metrics.*".

Acceptance

  • F1Score + f1Score() + binaryF1Score() public and in the API dump
  • All test cases above pass on JVM
  • Batches are walked once per update()
  • PR body states "unit tests only; ground-truth wiring tracked in SKEEP-005 (#1223)"

Notes

Reference: sklearn f1_score docs — https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html ;
torchmetrics F1Score — https://lightning.ai/docs/torchmetrics/stable/classification/f1_score.html .
Closing this issue is what unblocks the platform-parity lane (#1230–#1233) and review (#1235).

主要语言
Kotlin
星标
52
派生
15
平均合并
1 天 15 小时
30 天内合并 PR
36

环境准备

  • 没有 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 阅读贡献指南

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

SKaiNET-developers/SKaiNET 的其他 Issue

查看 SKaiNET-developers/SKaiNET 的全部 Issue

相似的 Issue

更多 Kotlin Issue

把新 issue 发到你的邮箱

精选适合新手参与的 GitHub issue 摘要。